Integrative analysis workshop with TCGAbiolinks and ELMER
Tiago Chedraoui Silva, Simon Coetzee, Ben Berman, Houtan Noushmehr
2017-07-13
Introduction
Another Bioconductor package we will also be introduce is called ELMER (L. Yao et al. 2015,Chedraoui Silva et al. (2017)) which allows one to identify DNA methylation changes in distal regulatory regions and correlate these signatures with expression of nearby genes to identify transcriptional targets associated with cancer. For these distal probes correlated with a gene, a transcription factor motif analysis is performed followed by expression analysis of transcription factors to infer upstream regulators.
We expect that participants of this workshop will understand the integrative analysis performed by using TCGAbiolinks + ELMER, as well as be able to execute it from the data acquisition process to the final interpretation of the results. The workshop assumes users with an intermediate level of familiarity with R, and basic understanding of tumor biology.
The figure below hihglights the workflow part which will be covered in this section. 
Loading GUI
First we will launch the GUI for TCGAbiolinks.
library(TCGAbiolinksGUI)
TCGAbiolinksGUI()Downloading data
Please download this two objects:
Analysis
Create MultiAssayExperiment object
To create the MultiAssayExperiment object go to Integrative analysis/ELMER/Create input data.
Select the DNA methylation object downloaded previously.
Select the gene expression object downloaded previously.
Fill the field Save as: and click on Create MAE object.
The object will be created.
Perform analysis
To perform ELMER analysis go to Integrative analysis/ELMER/Analysis.
Select the MAE data created in the previous section.
Select the groups that will be analysed: Primary solid Tumor and Solid Tissue Normal.
We will identify probes that are hypomethylated in Primary solid Tumor compared to Solid Tissue Normal.
For the significant differently methylated probes identified before we will correlated with the 20 nearest genes. Change the value of the field Number of permutations to 100, Raw P-value cut-off to 0.05 and Empirical P value cut-off to 0.01.
There will be no changes in the step 3.
There will be no changes in the step 4.
Click on Run the analysis.
If the analysis identified significant regulatory TF the results will be saved into an R object.
Visualize results
To visualize the results go to Integrative analysis/ELMER/Visualize results.
Click on Select results and select the object created on the previous section.
You will be able to visualize the correlation between DNA methyation levels and gene expression selecting a pair of gene and probe.
A probe and its near genes.
Or the avarage DNA methylation levels of probes of a Motif vs the expression of a TF.
For each enriched motif you can verify the ranking of sigificances between the correlation of DNA methylation level on the significant paired probes with that motif vs the TF expression (for all human TF).
The enrichement of each motif can be visualized.
You can take a look for a gene which was the probe linked.
You can see the plot and its neraby genes.
It is possible to visualize the table with the significant differently methylated probes.
It is possible to visualize the table with the pairs genes probes that have an negative correlation between DNA methyation levels and gene expression.
It is possible to visualize the table with the enriched motifs.
It is possible to visualize the table with the candidates regulatory TF.
Session Info
sessionInfo()## R version 3.4.0 (2017-04-21)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.5
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Bioc2017.TCGAbiolinks.ELMER_0.0.0.9000
## [2] SummarizedExperiment_1.6.3
## [3] DelayedArray_0.2.7
## [4] matrixStats_0.52.2
## [5] Biobase_2.36.2
## [6] GenomicRanges_1.28.3
## [7] GenomeInfoDb_1.12.2
## [8] IRanges_2.10.2
## [9] S4Vectors_0.14.3
## [10] BiocGenerics_0.22.0
## [11] TCGAbiolinks_2.5.6
## [12] bindrcpp_0.2
## [13] MultiAssayExperiment_1.2.1
## [14] dplyr_0.7.1
## [15] DT_0.2
## [16] ELMER_2.0.1
## [17] ELMER.data_2.0.1
##
## loaded via a namespace (and not attached):
## [1] shinydashboard_0.6.1 R.utils_2.5.0
## [3] RSQLite_2.0 AnnotationDbi_1.38.1
## [5] htmlwidgets_0.9 grid_3.4.0
## [7] trimcluster_0.1-2 BiocParallel_1.10.1
## [9] devtools_1.13.2 DESeq_1.28.0
## [11] munsell_0.4.3 codetools_0.2-15
## [13] withr_1.0.2 colorspace_1.3-2
## [15] BiocInstaller_1.26.0 knitr_1.16
## [17] robustbase_0.92-7 labeling_0.3
## [19] GenomeInfoDbData_0.99.0 KMsurv_0.1-5
## [21] mnormt_1.5-5 hwriter_1.3.2
## [23] bit64_0.9-7 rprojroot_1.2
## [25] downloader_0.4 biovizBase_1.24.0
## [27] ggthemes_3.4.0 EDASeq_2.10.0
## [29] diptest_0.75-7 R6_2.2.2
## [31] doParallel_1.0.10 locfit_1.5-9.1
## [33] AnnotationFilter_1.0.0 flexmix_2.3-14
## [35] reshape_0.8.6 bitops_1.0-6
## [37] assertthat_0.2.0 scales_0.4.1
## [39] nnet_7.3-12 gtable_0.2.0
## [41] ensembldb_2.0.3 rlang_0.1.1
## [43] genefilter_1.58.1 cmprsk_2.2-7
## [45] GlobalOptions_0.0.12 splines_3.4.0
## [47] rtracklayer_1.36.3 lazyeval_0.2.0
## [49] acepack_1.4.1 dichromat_2.0-0
## [51] selectr_0.3-1 broom_0.4.2
## [53] checkmate_1.8.3 yaml_2.1.14
## [55] reshape2_1.4.2 GenomicFeatures_1.28.4
## [57] backports_1.1.0 httpuv_1.3.5
## [59] Hmisc_4.0-3 tools_3.4.0
## [61] psych_1.7.5 ggplot2_2.2.1
## [63] RColorBrewer_1.1-2 Rcpp_0.12.11
## [65] plyr_1.8.4 base64enc_0.1-3
## [67] zlibbioc_1.22.0 purrr_0.2.2.2
## [69] RCurl_1.95-4.8 ggpubr_0.1.4
## [71] rpart_4.1-11 GetoptLong_0.1.6
## [73] viridis_0.4.0 zoo_1.8-0
## [75] ggrepel_0.6.5 cluster_2.0.6
## [77] magrittr_1.5 data.table_1.10.4
## [79] circlize_0.4.0 survminer_0.4.0
## [81] mvtnorm_1.0-6 whisker_0.3-2
## [83] ProtGenerics_1.8.0 aroma.light_3.6.0
## [85] hms_0.3 mime_0.5
## [87] evaluate_0.10.1 xtable_1.8-2
## [89] XML_3.98-1.9 mclust_5.3
## [91] gridExtra_2.2.1 shape_1.4.2
## [93] compiler_3.4.0 biomaRt_2.32.1
## [95] tibble_1.3.3 R.oo_1.21.0
## [97] htmltools_0.3.6 Formula_1.2-2
## [99] tidyr_0.6.3 geneplotter_1.54.0
## [101] DBI_0.7 matlab_1.0.2
## [103] ComplexHeatmap_1.14.0 MASS_7.3-47
## [105] fpc_2.1-10 BiocStyle_2.4.0
## [107] ShortRead_1.34.0 Matrix_1.2-10
## [109] readr_1.1.1 R.methodsS3_1.7.1
## [111] Gviz_1.20.0 bindr_0.1
## [113] km.ci_0.5-2 pkgconfig_2.0.1
## [115] GenomicAlignments_1.12.1 foreign_0.8-69
## [117] plotly_4.7.0 xml2_1.1.1
## [119] roxygen2_6.0.1 foreach_1.4.3
## [121] annotate_1.54.0 XVector_0.16.0
## [123] rvest_0.3.2 stringr_1.2.0
## [125] VariantAnnotation_1.22.3 digest_0.6.12
## [127] ConsensusClusterPlus_1.40.0 Biostrings_2.44.1
## [129] rmarkdown_1.6 survMisc_0.5.4
## [131] htmlTable_1.9 dendextend_1.5.2
## [133] edgeR_3.18.1 curl_2.7
## [135] kernlab_0.9-25 shiny_1.0.3
## [137] Rsamtools_1.28.0 commonmark_1.2
## [139] modeltools_0.2-21 rjson_0.2.15
## [141] nlme_3.1-131 jsonlite_1.5
## [143] viridisLite_0.2.0 limma_3.32.2
## [145] BSgenome_1.44.0 lattice_0.20-35
## [147] httr_1.2.1 DEoptimR_1.0-8
## [149] survival_2.41-3 interactiveDisplayBase_1.14.0
## [151] glue_1.1.1 UpSetR_1.3.3
## [153] prabclus_2.2-6 iterators_1.0.8
## [155] bit_1.1-12 class_7.3-14
## [157] stringi_1.1.5 blob_1.1.0
## [159] AnnotationHub_2.8.2 latticeExtra_0.6-28
## [161] memoise_1.1.0Bibliography
Chedraoui Silva, Tiago, Simon G. Coetzee, Lijing Yao, Dennis J. Hazelett, Houtan Noushmehr, and Benjamin P. Berman. 2017. “Enhancer Linking by Methylation/Expression Relationships with the R Package Elmer Version 2.” bioRxiv. Cold Spring Harbor Labs Journals. doi:10.1101/148726.
Yao, L, H Shen, PW Laird, PJ Farnham, and BP Berman. 2015. “Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes.” Genome Biology 16 (1): 105–5.